Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "223" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 30 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 30 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2460013 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.578311 | -0.986495 | -0.936412 | -0.272840 | -0.905144 | -0.882657 | 0.952124 | 10.125756 | 0.5589 | 0.5844 | 0.3549 | nan | nan |
| 2460012 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.420909 | -0.876901 | -1.090543 | -0.421621 | -0.846697 | -0.809665 | 1.019251 | 12.780218 | 0.5641 | 0.5886 | 0.3485 | nan | nan |
| 2460011 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.218971 | -1.317730 | -1.701449 | -0.341322 | -1.759015 | 24.722641 | 0.963285 | 15.738501 | 0.5872 | 0.5732 | 0.3385 | nan | nan |
| 2460010 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.572680 | 3.213398 | -1.457458 | 2.515851 | -1.141909 | 0.979279 | 0.677109 | 5.453007 | 0.5999 | 0.5458 | 0.3582 | nan | nan |
| 2460009 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.712691 | -0.262375 | -1.286970 | 1.753871 | -1.268866 | 7.397205 | -0.158620 | 4.033556 | 0.6040 | 0.5716 | 0.3527 | nan | nan |
| 2460008 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -1.608065 | -1.327396 | -1.643242 | -1.143361 | -0.729114 | 29.926970 | -0.875303 | 1.741663 | 0.6366 | 0.6396 | 0.3151 | nan | nan |
| 2460007 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.787062 | -0.664138 | -1.114936 | 0.180858 | -1.128616 | -0.591890 | 0.877537 | 1.615155 | 0.5992 | 0.6307 | 0.3423 | nan | nan |
| 2459999 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | nan | nan | nan | nan | nan | nan | nan | nan | 0.5827 | 0.6323 | 0.3313 | nan | nan |
| 2459998 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.988535 | -0.213860 | -1.026019 | 0.316529 | -1.368942 | -0.633423 | 1.157536 | 1.642301 | 0.5814 | 0.6206 | 0.3817 | nan | nan |
| 2459997 | RF_ok | 100.00% | 100.00% | 99.95% | 0.00% | - | - | 232.089025 | 232.496085 | inf | inf | 3285.790785 | 3246.874969 | 11439.124670 | 11086.449125 | 0.0266 | 0.2221 | 0.1667 | nan | nan |
| 2459996 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.635734 | -0.819661 | -0.857803 | -0.520105 | -1.048651 | 35.969433 | 0.662169 | 9.593281 | 0.6025 | 0.6107 | 0.3839 | nan | nan |
| 2459995 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.617347 | -0.698694 | -1.425219 | 0.143460 | -0.752258 | 4.507177 | 0.511688 | 2.358657 | 0.5952 | 0.6328 | 0.3819 | nan | nan |
| 2459994 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.670811 | -0.359243 | -1.268094 | 0.344936 | -0.631572 | -0.394147 | 0.849953 | 0.088593 | 0.5878 | 0.6272 | 0.3773 | nan | nan |
| 2459993 | RF_ok | 100.00% | 100.00% | 100.00% | 0.00% | - | - | 274.356537 | 274.191540 | inf | inf | 2635.247873 | 2613.935531 | 4162.904118 | 4027.409677 | nan | nan | nan | nan | nan |
| 2459991 | RF_ok | 100.00% | 99.95% | 99.95% | 0.00% | - | - | nan | nan | inf | inf | nan | nan | nan | nan | 0.3852 | 0.4429 | 0.3560 | nan | nan |
| 2459990 | RF_ok | 100.00% | 97.14% | 96.92% | 0.11% | - | - | 234.405594 | 234.608638 | inf | inf | 3224.873189 | 3097.697103 | 6041.874872 | 5562.902143 | 0.3346 | 0.3876 | 0.3092 | nan | nan |
| 2459989 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.720883 | -0.189113 | -1.070481 | 0.509192 | -0.560049 | -0.785400 | 0.918677 | -0.007409 | 0.5847 | 0.6254 | 0.3877 | nan | nan |
| 2459988 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.700691 | 0.004294 | -1.492852 | 0.655736 | -0.597379 | -0.315398 | 1.064399 | 0.064552 | 0.5851 | 0.6259 | 0.3776 | nan | nan |
| 2459987 | RF_ok | 100.00% | 93.03% | 92.87% | 0.05% | - | - | 171.502172 | 169.374511 | inf | inf | 2552.646447 | 2579.382600 | 4935.691719 | 5411.927519 | 0.3898 | 0.3944 | 0.3312 | nan | nan |
| 2459986 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.398709 | -0.153834 | -1.448384 | 0.566557 | -0.332904 | -0.165545 | 0.204840 | 0.502997 | 0.6115 | 0.6514 | 0.3407 | nan | nan |
| 2459985 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.689231 | -0.097124 | -1.310635 | 0.305124 | -0.752035 | -0.018191 | 1.356959 | 0.690585 | 0.5927 | 0.6304 | 0.3821 | nan | nan |
| 2459984 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.960050 | -0.506773 | -1.251943 | -0.036578 | -1.306700 | 18.598146 | -0.448319 | 0.542642 | 0.6123 | 0.6459 | 0.3607 | nan | nan |
| 2459983 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.182316 | -0.281893 | -1.368164 | 0.527987 | -1.038544 | -0.086296 | -0.054116 | -0.018780 | 0.6199 | 0.6604 | 0.3317 | nan | nan |
| 2459982 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.604318 | -0.847286 | -0.960201 | -0.029233 | -1.148974 | -1.101498 | -0.875881 | -0.934788 | 0.6844 | 0.6927 | 0.2865 | nan | nan |
| 2459981 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.076232 | -0.314108 | -1.579567 | 0.753803 | -0.427473 | -0.390942 | 0.628816 | 2.259203 | 0.5977 | 0.6331 | 0.3809 | nan | nan |
| 2459980 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.151959 | -0.774696 | -1.534824 | 0.118107 | -1.114431 | -0.991187 | -1.034550 | -0.031612 | 0.6416 | 0.6659 | 0.3056 | nan | nan |
| 2459979 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.058385 | -0.569053 | -1.534641 | 0.188024 | -0.674079 | -1.150934 | 1.045293 | 2.247288 | 0.5875 | 0.6278 | 0.3839 | nan | nan |
| 2459978 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.050480 | -0.438794 | -1.555237 | 0.412312 | -0.516237 | -0.774471 | 1.384222 | 3.241894 | 0.5876 | 0.6264 | 0.3905 | nan | nan |
| 2459977 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.030051 | -0.399974 | -1.510827 | 0.194657 | -0.884136 | -0.659010 | 0.460122 | 2.372375 | 0.5517 | 0.5892 | 0.3500 | nan | nan |
| 2459976 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -1.051269 | -0.204526 | -1.588337 | 0.327771 | -0.517094 | -0.786665 | 0.405089 | 2.001899 | 0.5961 | 0.6336 | 0.3799 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 10.125756 | -1.578311 | -0.986495 | -0.936412 | -0.272840 | -0.905144 | -0.882657 | 0.952124 | 10.125756 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 12.780218 | -1.420909 | -0.876901 | -1.090543 | -0.421621 | -0.846697 | -0.809665 | 1.019251 | 12.780218 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 24.722641 | -1.218971 | -1.317730 | -1.701449 | -0.341322 | -1.759015 | 24.722641 | 0.963285 | 15.738501 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 5.453007 | -1.572680 | 3.213398 | -1.457458 | 2.515851 | -1.141909 | 0.979279 | 0.677109 | 5.453007 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 7.397205 | -1.712691 | -0.262375 | -1.286970 | 1.753871 | -1.268866 | 7.397205 | -0.158620 | 4.033556 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 29.926970 | -1.327396 | -1.608065 | -1.143361 | -1.643242 | 29.926970 | -0.729114 | 1.741663 | -0.875303 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 1.615155 | -0.787062 | -0.664138 | -1.114936 | 0.180858 | -1.128616 | -0.591890 | 0.877537 | 1.615155 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Shape | nan | nan | nan | nan | nan | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 1.642301 | -0.988535 | -0.213860 | -1.026019 | 0.316529 | -1.368942 | -0.633423 | 1.157536 | 1.642301 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Power | inf | 232.089025 | 232.496085 | inf | inf | 3285.790785 | 3246.874969 | 11439.124670 | 11086.449125 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 35.969433 | -0.635734 | -0.819661 | -0.857803 | -0.520105 | -1.048651 | 35.969433 | 0.662169 | 9.593281 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 4.507177 | -0.617347 | -0.698694 | -1.425219 | 0.143460 | -0.752258 | 4.507177 | 0.511688 | 2.358657 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Temporal Discontinuties | 0.849953 | -0.670811 | -0.359243 | -1.268094 | 0.344936 | -0.631572 | -0.394147 | 0.849953 | 0.088593 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Power | inf | 274.356537 | 274.191540 | inf | inf | 2635.247873 | 2613.935531 | 4162.904118 | 4027.409677 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Shape | nan | nan | nan | inf | inf | nan | nan | nan | nan |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Power | inf | 234.608638 | 234.405594 | inf | inf | 3097.697103 | 3224.873189 | 5562.902143 | 6041.874872 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Temporal Discontinuties | 0.918677 | -0.189113 | -0.720883 | 0.509192 | -1.070481 | -0.785400 | -0.560049 | -0.007409 | 0.918677 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Temporal Discontinuties | 1.064399 | 0.004294 | -0.700691 | 0.655736 | -1.492852 | -0.315398 | -0.597379 | 0.064552 | 1.064399 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Power | inf | 171.502172 | 169.374511 | inf | inf | 2552.646447 | 2579.382600 | 4935.691719 | 5411.927519 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Power | 0.566557 | -0.153834 | -0.398709 | 0.566557 | -1.448384 | -0.165545 | -0.332904 | 0.502997 | 0.204840 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | ee Temporal Discontinuties | 1.356959 | -0.097124 | -0.689231 | 0.305124 | -1.310635 | -0.018191 | -0.752035 | 0.690585 | 1.356959 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Variability | 18.598146 | -0.960050 | -0.506773 | -1.251943 | -0.036578 | -1.306700 | 18.598146 | -0.448319 | 0.542642 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Power | 0.527987 | -1.182316 | -0.281893 | -1.368164 | 0.527987 | -1.038544 | -0.086296 | -0.054116 | -0.018780 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Power | -0.029233 | -0.604318 | -0.847286 | -0.960201 | -0.029233 | -1.148974 | -1.101498 | -0.875881 | -0.934788 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 2.259203 | -0.314108 | -1.076232 | 0.753803 | -1.579567 | -0.390942 | -0.427473 | 2.259203 | 0.628816 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Power | 0.118107 | -0.774696 | -1.151959 | 0.118107 | -1.534824 | -0.991187 | -1.114431 | -0.031612 | -1.034550 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 2.247288 | -1.058385 | -0.569053 | -1.534641 | 0.188024 | -0.674079 | -1.150934 | 1.045293 | 2.247288 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 3.241894 | -0.438794 | -1.050480 | 0.412312 | -1.555237 | -0.774471 | -0.516237 | 3.241894 | 1.384222 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 2.372375 | -1.030051 | -0.399974 | -1.510827 | 0.194657 | -0.884136 | -0.659010 | 0.460122 | 2.372375 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 223 | N19 | RF_ok | nn Temporal Discontinuties | 2.001899 | -0.204526 | -1.051269 | 0.327771 | -1.588337 | -0.786665 | -0.517094 | 2.001899 | 0.405089 |